Computationally Efficient Approximations for Matrix-Based Rényi's Entropy

Gong, Tieliang; Dong, Yuxin; Yu, Shujian; Dong, Bo

doi:10.1109/tsp.2022.3233724

Cited by 2 publications

(1 citation statement)

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“…As shown in Figure 8, the time consumption of the estimators scales up with an increase in sample size. Because of the trace operator on Gram matrix (G) to power α (tr(G α )) in TE θ kα , it poses a great challenge in terms of both storage and computing when using TE θ kα in practice [45]. In TE θ bin , a sample binning method rather than more complex technology is used to reconstruct the time series state-space, so, compared with other methods, the time cost of TE θ bin is within an acceptable range.…”

Section: Discussionmentioning

confidence: 99%

Performance Baseline of Phase Transfer Entropy Methods for Detecting Animal Brain Area Interactions

Zhu

Zhang

et al. 2023

Entropy

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Objective: Phase transfer entropy (TEθ) methods perform well in animal sensory–spatial associative learning. However, their advantages and disadvantages remain unclear, constraining their usage. Method: This paper proposes the performance baseline of the TEθ methods. Specifically, four TEθ methods are applied to the simulated signals generated by a neural mass model and the actual neural data from ferrets with known interaction properties to investigate the accuracy, stability, and computational complexity of the TEθ methods in identifying the directional coupling. Then, the most suitable method is selected based on the performance baseline and used on the local field potential recorded from pigeons to detect the interaction between the hippocampus (Hp) and nidopallium caudolaterale (NCL) in visual–spatial associative learning. Results: (1) This paper obtains a performance baseline table that contains the most suitable method for different scenarios. (2) The TEθ method identifies an information flow preferentially from Hp to NCL of pigeons at the θ band (4–12 Hz) in visual–spatial associative learning. Significance: These outcomes provide a reference for the TEθ methods in detecting the interactions between brain areas.

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Section: Discussionmentioning

confidence: 99%